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From biology to robotics and back
Manos Angelidis
Simulation Software Engineer @ The Human Brain
Project - Neurorobotics Platform
Introduction
▶ Inspiration from nature has been one of the
cornerstones of scientific thought
▶ Biological processes can be observed and
translated into useful technologies
▶ What I cannot create I cannot understand -
R.Feynman
▶ Developing biotechnologies can help us
understand nature
Research and technology fields
▶ Biomaterials
▶ Biotechnology - biochemistry
▶ Biorobotics
▶ Biomechanics
▶ Bio-inspired AI
▶ Neuromorphic computing
A systematic approach to biologically inspired
research: BioRobotics
▶ Biorobotics lies in the intersection
between Neuroscience, Robotics and AI
▶ Basic idea is the “creation of machines
that imitate biological systems”
▶ Subcategories
• Locomotion
• Soft robotics
• Implants
• Neurorobotics
▶ Friends:
• Human - Robot interaction
• Brain - Machine interfaces
▶ End goal: Intelligent robots
A body to test our hypotheses on cognition
▶ All our hypotheses on human level cognition need a
body to test their validity
▶ The brain is in constant interaction with the environment
▶ Sensorimotor integration is the basis behind intelligent
robotics
▶ Whatever we test on robots can give us insight on how it
works on the body
▶ Various non-biological successful control methods
(PIDs, fuzzy-logic, neural nets)
• Path planning
• Motion planning
▶ Higher level cognitive controllers - incorporate high
level decision making
▶ The brain is the best controller
• Flexible - can learn different tasks with ease
• Memory efficient - stores only what is
important
• Low energy consumption
• Contains tons of a priori knowledge coming
from billions of years of evolution
Do as the brain does
The need to simulate robots in virtual environments
▶ Impossible to obtain experimental data in many situations
▶ Impossible to perform millions of experiments in parallel
▶ Setting up complex experimental environments is costly and requires a lot of know-how
First step - Sensory integration
▶ Perception of the environment is the first step in closed-loop control problems
▶ The brain processes different kinds of signals with the same “hardware”
▶ Common sensors used in closed loop intelligent robotics
•Lidars
•Cameras
•Proprioception - limb positioning
•Touch feedback
•Sounds
•Smells
Second step - physically realistic simulation
▶ Simulating environments - realistic physics
• ODE
• Bullet
• Nvidia PhysX
• Dart
• Simbody
• Mujoco
Third Step - AI control algorithms
▶ Neural Nets
▶ Artificial Brains
▶ High level controllers
▶ Reinforcement learning
Putting it all together
▶ Realistic physical simulation of complex phenomena (fluids, rigids, soft bodies)
▶ Sensory integration
▶ Control algorithms
▶ Synchronization mechanisms
▶ Cool visualization
One implementation - The Neurorobotics Platform
The impact of biology study on AI
▶ Neural Networks - Brain Neurons
▶ Evolutionary algorithms - Biological species evolution
▶ Reinforcement learning - reward based systems - psychological behaviourism
▶ Cognitive robotics - Intelligent Systems
▶ Vision Systems - Visual cortex layered representations
▶ Working memory models - LSTMs
Neural Networks and their relation to the brain
▶ RNNs - Recurrent neurons are observed in the brain
▶ CNNs - visual cortex layered representations
▶ Feedforward ANNs - Abstraction of Neurons
Neurally Inspired Computation
▶ The Neuron as a computational unit - Spikes as
information carriers
▶ Networks of biological neurons - Spiking Neural
Networks
▶ Networks of abstract neurons (averaged firing rate)
Neuromorphic hardware
▶ Intel Loihi
▶ IBM TrueNorth - currently trying other architectures
▶ SpiNNaker / BrainScale
Examples of successful projects
▶ EPFL Salamandra Robotica
▶ Nengo Robot adaptive control
▶ Intel loihi object detection
▶ Teaching a robot to walk
Why is it important for IT
▶ Supercomputing
▶ Hardware
▶ Algorithms
▶ Databases
The future of bioinspired AI
▶ Better and specialized hardware - Intel Loihi, IBM True North, Spinnaker, companies developing
hardware
▶ Better software - Ecosystem growth
▶ Better testing environments - AI gym, pybullet etc
▶ Better algorithms
▶ Better architectures
▶ More data
▶ One-shot learning
▶ Unsupervised learning
▶ Learning to learn
Conclusions
▶ Multidisciplinary research is the only viable approach for modern science
▶ Biology an be a source of unlimited inspiration
▶ AI methods and robotics can learn a lot from the brain

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From biology to robotics and back by Manos Angelidis

  • 1. From biology to robotics and back Manos Angelidis Simulation Software Engineer @ The Human Brain Project - Neurorobotics Platform
  • 2. Introduction ▶ Inspiration from nature has been one of the cornerstones of scientific thought ▶ Biological processes can be observed and translated into useful technologies ▶ What I cannot create I cannot understand - R.Feynman ▶ Developing biotechnologies can help us understand nature
  • 3. Research and technology fields ▶ Biomaterials ▶ Biotechnology - biochemistry ▶ Biorobotics ▶ Biomechanics ▶ Bio-inspired AI ▶ Neuromorphic computing
  • 4. A systematic approach to biologically inspired research: BioRobotics ▶ Biorobotics lies in the intersection between Neuroscience, Robotics and AI ▶ Basic idea is the “creation of machines that imitate biological systems” ▶ Subcategories • Locomotion • Soft robotics • Implants • Neurorobotics ▶ Friends: • Human - Robot interaction • Brain - Machine interfaces ▶ End goal: Intelligent robots
  • 5.
  • 6. A body to test our hypotheses on cognition ▶ All our hypotheses on human level cognition need a body to test their validity ▶ The brain is in constant interaction with the environment ▶ Sensorimotor integration is the basis behind intelligent robotics ▶ Whatever we test on robots can give us insight on how it works on the body
  • 7. ▶ Various non-biological successful control methods (PIDs, fuzzy-logic, neural nets) • Path planning • Motion planning ▶ Higher level cognitive controllers - incorporate high level decision making ▶ The brain is the best controller • Flexible - can learn different tasks with ease • Memory efficient - stores only what is important • Low energy consumption • Contains tons of a priori knowledge coming from billions of years of evolution Do as the brain does
  • 8.
  • 9. The need to simulate robots in virtual environments ▶ Impossible to obtain experimental data in many situations ▶ Impossible to perform millions of experiments in parallel ▶ Setting up complex experimental environments is costly and requires a lot of know-how
  • 10. First step - Sensory integration ▶ Perception of the environment is the first step in closed-loop control problems ▶ The brain processes different kinds of signals with the same “hardware” ▶ Common sensors used in closed loop intelligent robotics •Lidars •Cameras •Proprioception - limb positioning •Touch feedback •Sounds •Smells
  • 11. Second step - physically realistic simulation ▶ Simulating environments - realistic physics • ODE • Bullet • Nvidia PhysX • Dart • Simbody • Mujoco
  • 12. Third Step - AI control algorithms ▶ Neural Nets ▶ Artificial Brains ▶ High level controllers ▶ Reinforcement learning
  • 13. Putting it all together ▶ Realistic physical simulation of complex phenomena (fluids, rigids, soft bodies) ▶ Sensory integration ▶ Control algorithms ▶ Synchronization mechanisms ▶ Cool visualization
  • 14. One implementation - The Neurorobotics Platform
  • 15. The impact of biology study on AI ▶ Neural Networks - Brain Neurons ▶ Evolutionary algorithms - Biological species evolution ▶ Reinforcement learning - reward based systems - psychological behaviourism ▶ Cognitive robotics - Intelligent Systems ▶ Vision Systems - Visual cortex layered representations ▶ Working memory models - LSTMs
  • 16. Neural Networks and their relation to the brain ▶ RNNs - Recurrent neurons are observed in the brain ▶ CNNs - visual cortex layered representations ▶ Feedforward ANNs - Abstraction of Neurons
  • 17. Neurally Inspired Computation ▶ The Neuron as a computational unit - Spikes as information carriers ▶ Networks of biological neurons - Spiking Neural Networks ▶ Networks of abstract neurons (averaged firing rate)
  • 18. Neuromorphic hardware ▶ Intel Loihi ▶ IBM TrueNorth - currently trying other architectures ▶ SpiNNaker / BrainScale
  • 19. Examples of successful projects ▶ EPFL Salamandra Robotica ▶ Nengo Robot adaptive control ▶ Intel loihi object detection ▶ Teaching a robot to walk
  • 20. Why is it important for IT ▶ Supercomputing ▶ Hardware ▶ Algorithms ▶ Databases
  • 21. The future of bioinspired AI ▶ Better and specialized hardware - Intel Loihi, IBM True North, Spinnaker, companies developing hardware ▶ Better software - Ecosystem growth ▶ Better testing environments - AI gym, pybullet etc ▶ Better algorithms ▶ Better architectures ▶ More data ▶ One-shot learning ▶ Unsupervised learning ▶ Learning to learn
  • 22. Conclusions ▶ Multidisciplinary research is the only viable approach for modern science ▶ Biology an be a source of unlimited inspiration ▶ AI methods and robotics can learn a lot from the brain